Importing Dataset

import pandas as pd
df=pd.read_csv("data.csv")
df.head(5)

	id	diagnosis	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	...	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst	fractal_dimension_worst	Unnamed: 32
0	842302	M	17.99	10.38	122.80	1001.0	0.11840	0.27760	0.3001	0.14710	...	17.33	184.60	2019.0	0.1622	0.6656	0.7119	0.2654	0.4601	0.11890	NaN
1	842517	M	20.57	17.77	132.90	1326.0	0.08474	0.07864	0.0869	0.07017	...	23.41	158.80	1956.0	0.1238	0.1866	0.2416	0.1860	0.2750	0.08902	NaN
2	84300903	M	19.69	21.25	130.00	1203.0	0.10960	0.15990	0.1974	0.12790	...	25.53	152.50	1709.0	0.1444	0.4245	0.4504	0.2430	0.3613	0.08758	NaN
3	84348301	M	11.42	20.38	77.58	386.1	0.14250	0.28390	0.2414	0.10520	...	26.50	98.87	567.7	0.2098	0.8663	0.6869	0.2575	0.6638	0.17300	NaN
4	84358402	M	20.29	14.34	135.10	1297.0	0.10030	0.13280	0.1980	0.10430	...	16.67	152.20	1575.0	0.1374	0.2050	0.4000	0.1625	0.2364	0.07678	NaN

5 rows × 33 columns

Checking NULL values

df.isna().sum()

	0
id	0
diagnosis	0
radius_mean	0
texture_mean	0
perimeter_mean	0
area_mean	0
smoothness_mean	0
compactness_mean	0
concavity_mean	0
concave points_mean	0
symmetry_mean	0
fractal_dimension_mean	0
radius_se	0
texture_se	0
perimeter_se	0
area_se	0
smoothness_se	0
compactness_se	0
concavity_se	0
concave points_se	0
symmetry_se	0
fractal_dimension_se	0
radius_worst	0
texture_worst	0
perimeter_worst	0
area_worst	0
smoothness_worst	0
compactness_worst	0
concavity_worst	0
concave points_worst	0
symmetry_worst	0
fractal_dimension_worst	0
Unnamed: 32	569

dtype: int64

Dropping the last column that is having all the null values

import pandas as pd

df=pd.read_csv("data.csv")
df.head(10)
df.isna().sum()

# Dropping the last column
df = df.iloc[:, :-1]

df = df.iloc[:, :-1]

df.head()

	id	diagnosis	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	...	fractal_dimension_se	radius_worst	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst
0	842302	M	17.99	10.38	122.80	1001.0	0.11840	0.27760	0.3001	0.14710	...	0.006193	25.38	17.33	184.60	2019.0	0.1622	0.6656	0.7119	0.2654	0.4601
1	842517	M	20.57	17.77	132.90	1326.0	0.08474	0.07864	0.0869	0.07017	...	0.003532	24.99	23.41	158.80	1956.0	0.1238	0.1866	0.2416	0.1860	0.2750
2	84300903	M	19.69	21.25	130.00	1203.0	0.10960	0.15990	0.1974	0.12790	...	0.004571	23.57	25.53	152.50	1709.0	0.1444	0.4245	0.4504	0.2430	0.3613
3	84348301	M	11.42	20.38	77.58	386.1	0.14250	0.28390	0.2414	0.10520	...	0.009208	14.91	26.50	98.87	567.7	0.2098	0.8663	0.6869	0.2575	0.6638
4	84358402	M	20.29	14.34	135.10	1297.0	0.10030	0.13280	0.1980	0.10430	...	0.005115	22.54	16.67	152.20	1575.0	0.1374	0.2050	0.4000	0.1625	0.2364

5 rows × 31 columns

df.isna().sum()

	0
id	0
diagnosis	0
radius_mean	0
texture_mean	0
perimeter_mean	0
area_mean	0
smoothness_mean	0
compactness_mean	0
concavity_mean	0
concave points_mean	0
symmetry_mean	0
fractal_dimension_mean	0
radius_se	0
texture_se	0
perimeter_se	0
area_se	0
smoothness_se	0
compactness_se	0
concavity_se	0
concave points_se	0
symmetry_se	0
fractal_dimension_se	0
radius_worst	0
texture_worst	0
perimeter_worst	0
area_worst	0
smoothness_worst	0
compactness_worst	0
concavity_worst	0
concave points_worst	0
symmetry_worst	0

dtype: int64

Data Describe

df.describe()

	id	radius_mean	texture_mean	perimeter_mean	area_mean	smoothness_mean	compactness_mean	concavity_mean	concave points_mean	symmetry_mean	...	fractal_dimension_se	radius_worst	texture_worst	perimeter_worst	area_worst	smoothness_worst	compactness_worst	concavity_worst	concave points_worst	symmetry_worst
count	5.690000e+02	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	...	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000	569.000000
mean	3.037183e+07	14.127292	19.289649	91.969033	654.889104	0.096360	0.104341	0.088799	0.048919	0.181162	...	0.003795	16.269190	25.677223	107.261213	880.583128	0.132369	0.254265	0.272188	0.114606	0.290076
std	1.250206e+08	3.524049	4.301036	24.298981	351.914129	0.014064	0.052813	0.079720	0.038803	0.027414	...	0.002646	4.833242	6.146258	33.602542	569.356993	0.022832	0.157336	0.208624	0.065732	0.061867
min	8.670000e+03	6.981000	9.710000	43.790000	143.500000	0.052630	0.019380	0.000000	0.000000	0.106000	...	0.000895	7.930000	12.020000	50.410000	185.200000	0.071170	0.027290	0.000000	0.000000	0.156500
25%	8.692180e+05	11.700000	16.170000	75.170000	420.300000	0.086370	0.064920	0.029560	0.020310	0.161900	...	0.002248	13.010000	21.080000	84.110000	515.300000	0.116600	0.147200	0.114500	0.064930	0.250400
50%	9.060240e+05	13.370000	18.840000	86.240000	551.100000	0.095870	0.092630	0.061540	0.033500	0.179200	...	0.003187	14.970000	25.410000	97.660000	686.500000	0.131300	0.211900	0.226700	0.099930	0.282200
75%	8.813129e+06	15.780000	21.800000	104.100000	782.700000	0.105300	0.130400	0.130700	0.074000	0.195700	...	0.004558	18.790000	29.720000	125.400000	1084.000000	0.146000	0.339100	0.382900	0.161400	0.317900
max	9.113205e+08	28.110000	39.280000	188.500000	2501.000000	0.163400	0.345400	0.426800	0.201200	0.304000	...	0.029840	36.040000	49.540000	251.200000	4254.000000	0.222600	1.058000	1.252000	0.291000	0.663800

8 rows × 30 columns

Data Info

df.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 569 entries, 0 to 568
Data columns (total 31 columns):
 #   Column                  Non-Null Count  Dtype  
---  ------                  --------------  -----  
 0   id                      569 non-null    int64  
 1   diagnosis               569 non-null    object 
 2   radius_mean             569 non-null    float64
 3   texture_mean            569 non-null    float64
 4   perimeter_mean          569 non-null    float64
 5   area_mean               569 non-null    float64
 6   smoothness_mean         569 non-null    float64
 7   compactness_mean        569 non-null    float64
 8   concavity_mean          569 non-null    float64
 9   concave points_mean     569 non-null    float64
 10  symmetry_mean           569 non-null    float64
 11  fractal_dimension_mean  569 non-null    float64
 12  radius_se               569 non-null    float64
 13  texture_se              569 non-null    float64
 14  perimeter_se            569 non-null    float64
 15  area_se                 569 non-null    float64
 16  smoothness_se           569 non-null    float64
 17  compactness_se          569 non-null    float64
 18  concavity_se            569 non-null    float64
 19  concave points_se       569 non-null    float64
 20  symmetry_se             569 non-null    float64
 21  fractal_dimension_se    569 non-null    float64
 22  radius_worst            569 non-null    float64
 23  texture_worst           569 non-null    float64
 24  perimeter_worst         569 non-null    float64
 25  area_worst              569 non-null    float64
 26  smoothness_worst        569 non-null    float64
 27  compactness_worst       569 non-null    float64
 28  concavity_worst         569 non-null    float64
 29  concave points_worst    569 non-null    float64
 30  symmetry_worst          569 non-null    float64
dtypes: float64(29), int64(1), object(1)
memory usage: 137.9+ KB

df.shape

(569, 31)

df.columns

Index(['id', 'diagnosis', 'radius_mean', 'texture_mean', 'perimeter_mean',
       'area_mean', 'smoothness_mean', 'compactness_mean', 'concavity_mean',
       'concave points_mean', 'symmetry_mean', 'fractal_dimension_mean',
       'radius_se', 'texture_se', 'perimeter_se', 'area_se', 'smoothness_se',
       'compactness_se', 'concavity_se', 'concave points_se', 'symmetry_se',
       'fractal_dimension_se', 'radius_worst', 'texture_worst',
       'perimeter_worst', 'area_worst', 'smoothness_worst',
       'compactness_worst', 'concavity_worst', 'concave points_worst',
       'symmetry_worst'],
      dtype='object')

Using Matplotlib Subplots

import matplotlib.pyplot as plt
import pandas as pd

# Assuming your DataFrame is named 'df'
# and you have columns 'col1', 'col2', 'col3', etc.

fig, axes = plt.subplots(len(df.columns), 1, figsize=(10, len(df.columns) * 5))  # Adjust figsize as needed

for i, col in enumerate(df.columns):
    axes[i].hist(df[col], bins=20)  # Example: histogram, you can change plot type
    axes[i].set_title(f'Distribution of {col}')
    axes[i].set_xlabel(col)
    axes[i].set_ylabel('Frequency')

plt.tight_layout()
plt.show()

Using Seaborn

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns


# Using Seaborn for more advanced visualizations
for col in df.columns:
    plt.figure(figsize=(8, 6))
    sns.histplot(df[col], kde=True, bins=20)  # KDE plot added for smoother distribution
    plt.title(f'Distribution of {col}')
    plt.xlabel(col)
    plt.ylabel('Frequency')
    plt.show()

# Example of a pairplot (relationship between all pairs of columns)
sns.pairplot(df)
plt.show()

# Example of a boxplot
for col in df.columns:
  plt.figure(figsize=(8, 6))
  sns.boxplot(y=df[col])
  plt.title(f'Boxplot of {col}')
  plt.show()

Using Violin Plots

# Example of a violin plot
for col in df.columns:
  plt.figure(figsize=(8, 6))
  sns.violinplot(y=df[col])
  plt.title(f'Violin Plot of {col}')
  plt.show()

import seaborn as sns
import matplotlib.pyplot as plt
df = df.drop('diagnosis', axis=1)
numerical_df = df.select_dtypes(include=['number'])
correlation_matrix = df.corr()

Correlation Matrix

import seaborn as sns
import matplotlib.pyplot as plt

plt.figure(figsize=(15, 10))
sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm', fmt=".2f")
plt.title('Correlation Matrix')
plt.show()

Covariance between radius mean and area mean

import pandas as pd

covariance = df['radius_mean'].cov(df['area_mean'])
print(f"Covariance between radius mean and area mean: {covariance}")

Covariance between radius mean and area mean: 1224.483409346457

Covariance between radius mean and fractal dimension se

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns


# Calculate the covariance between 'radius_mean' and 'fractal_dimension_se'
covariance = df['radius_mean'].cov(df['fractal_dimension_se'])

print(f"Covariance between radius mean and fractal dimension se: {covariance}")

Covariance between radius mean and fractal dimension se: -0.0003976248576440629

Pearson Correlation

import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

# Calculate the mean of 'area_mean'
area_mean = df['area_mean'].mean()
print(f"Mean of area_mean: {area_mean}")

# Calculate the mean of 'radius_mean'
radius_mean = df['radius_mean'].mean()
print(f"Mean of radius_mean: {radius_mean}")
# Calculate Pearson correlation between 'radius_mean' and 'area_mean'
correlation = df['radius_mean'].corr(df['area_mean'])
print(f"Pearson correlation between radius_mean and area_mean: {correlation}")

Mean of area_mean: 654.8891036906855
Mean of radius_mean: 14.127291739894552
Pearson correlation between radius_mean and area_mean: 0.9873571700566127

import pandas as pd
from scipy import stats
df=pd.read_csv("data.csv")
t_statistic, p_value = stats.ttest_1samp(df['radius_mean'], 10)
print(f"One-sample t-test results for 'radius_mean':")
print(f"  t-statistic: {t_statistic}")
print(f"  p-value: {p_value}")


group1 = df[df['diagnosis'] == 'M']['radius_mean']
group2 = df[df['diagnosis'] == 'B']['radius_mean']
t_statistic, p_value = stats.ttest_ind(group1, group2)
print(f"\nTwo-sample t-test results for 'radius_mean':")
print(f"  t-statistic: {t_statistic}")
print(f"  p-value: {p_value}")

One-sample t-test results for 'radius_mean':
  t-statistic: 27.936975344092367
  p-value: 1.0054149051956924e-108

Two-sample t-test results for 'radius_mean':
  t-statistic: 25.435821610057054
  p-value: 8.465940572262422e-96